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mnabihali / pytorch_MLP_for_ASR

This code implements a basic MLP for speech recognition. The MLP is trained with pytorch, while feature extraction, alignments, and decoding are performed with Kaldi. The current implementation supports dropout and batch normalization. An example for phoneme recognition using the standard TIMIT dataset is provided.

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Introduction:

This code implements a basic MLP for HMM-DNN speech recognition. The MLP is trained with pytorch, while feature extraction, alignments, and decoding are performed with Kaldi. The current implementation supports dropout and batch normalization. An example for phoneme recognition using the standard TIMIT dataset is provided.

Prerequisites:

  • Make sure that python is installed (the code is tested with python 2.7). Even though not mandatory, we suggest to use Anaconda (https://anaconda.org/anaconda/python).

  • If not already done, install pytorch (http://pytorch.org/) and make sure that the installation works. As a first test, type “python” and, once entered into the console, type “import torch”. Make sure everything is fine.

  • If not already done, install Kaldi (http://kaldi-asr.org/). As suggested during the installation, do not forget to add the path of the Kaldi binaries into $HOME/.bashrc. As a first test to check the installation, open a bash shell, type “copy-feats” and make sure no errors appear.

  • Install kaldi-io package from the kaldi-io-for-python project (https://github.com/vesis84/kaldi-io-for-python). It provides a simple interface between kaldi and python. To install it:

  1. run git clone https://github.com/vesis84/kaldi-io-for-python.git
  2. add PYTHONPATH=${PYTHONPATH}: to $HOME/.bashrc
  3. now type import kaldi_io from the python console and make sure the package is correctly imported. You can find more info (including some reading and writing tests) on https://github.com/vesis84/kaldi-io-for-python

The code has been tested with:

  • Python 2.7
  • Ubuntu 17.04
  • Pytorch 0.3
  • Cuda 9.1

How to run a TIMIT experiment:

1. Run the Kaldi s5 baseline of TIMIT.

This step is necessary to compute features and labels later used to train the pytorch MLP. In particular:

  • go to $KALDI_ROOT/egs/timit/s5.
  • run the script run.sh. Make sure everything works fine. Please, also run the Karel’s DNN baseline using local/nnet/run_dnn.sh.
  • Compute the alignments for test and dev data with the following commands.

If you wanna use tri3 alignments, type:

steps/align_fmllr.sh --nj 4 data/dev data/lang exp/tri3 exp/tri3_ali_dev

steps/align_fmllr.sh --nj 4 data/test data/lang exp/tri3 exp/tri3_ali_test

If you wanna use dnn alignments (as suggested), type:

steps/nnet/align.sh --nj 4 data-fmllr-tri3/dev data/lang exp/dnn4_pretrain-dbn_dnn exp/dnn4_pretrain-dbn_dnn_ali_dev

steps/nnet/align.sh --nj 4 data-fmllr-tri3/test data/lang exp/dnn4_pretrain-dbn_dnn exp/dnn4_pretrain-dbn_dnn_ali_test

2. Split the feature lists into chunks.

Go to the pytorch_MLP_for_ASR folder. The create_chunks.sh script first shuffles or sorts (based on the sentence length) a kaldi feature list and then split it into a certain number of chunks. Shuffling a list could be good for feed-forward DNNs, while a sorted list can be useful for RNNs (not used here). The code also computes per-speaker and per-sentence CMVN.

For mfcc features run:

./create_chunks.sh $KALDI_ROOT/egs/timit/s5/data/train mfcc_lists 5 train 0
./create_chunks.sh $KALDI_ROOT/egs/timit/s5/data/dev mfcc_lists 1 dev 0
./create_chunks.sh $KALDI_ROOT/egs/timit/s5/data/test mfcc_lists 1 test 0

For fMLLR features run:

./create_chunks.sh $KALDI_ROOT/egs/timit/s5/data-fmllr-tri3/train fmllr_lists 5 train 0
./create_chunks.sh $KALDI_ROOT/egs/timit/s5/data-fmllr-tri3/dev fmllr_lists 1 dev 0
./create_chunks.sh $KALDI_ROOT/egs/timit/s5/data-fmllr-tri3/test fmllr_lists 1 test 0

3. Setup the Config file.

  • Open the files TIMIT_MLP_mfcc.cfg,TIMIT_MLP_fmllr.cfg and modify them according to your paths.
  1. tr_fea_scp contains the list of features created with create_chunks.sh.
  2. tr_fea_opts allows users to easily add normalizations, derivatives and other types of feature processing (see for instance TIMIT_MLP_mfcc.cfg).
  3. tr_lab_folder is the kaldi folder containing the alignments (labels).
  4. tr_lab_opts allows users to derive context-dependent phone targets (when set to ali-to-pdf) or monophone targets (when set to ali-to-phones --per-frame).
  5. Modify the paths for dev and test data.
  6. Feel free to modify the DNN architecture and the other optimization parameters according to your needs.
  7. The required count_file (used to normalize the DNN posteriors before feeding the decoder and automaticallt created by kadldi when running s5 recipe) can be found here: $KALDI_ROOT/egs/timit/s5/exp/dnn4_pretrain-dbn_dnn/ali_train_pdf.counts.
  8. Use the option use_cuda=1 for running the code on a GPU (strongly suggested).
  9. Use the option save_gpumem=0 to save gpu memory. The code will be a little bit slower (about 10-15%), but it saves gpu memory. Use save_gpumem=1 only if your GPU has more that 2GB of memory.

4. Train the DNN.

  • Type the following command to run DNN training :
python MLP_ASR.py --cfg TIMIT_MLP_mfcc.cfg 2> log.log

or

python MLP_ASR.py --cfg TIMIT_MLP_fmllr.cfg 2> log.log

If everything is working fine, your output (for fMLLR features) should look like this:

epoch 1 training_cost=3.185270, training_error=0.690495, dev_error=0.549124, test_error=0.549172, learning_rate=0.080000, execution_time(s)=85.436095
epoch 2 training_cost=1.950891, training_error=0.533513, dev_error=0.498461, test_error=0.499940, learning_rate=0.080000, execution_time(s)=78.238582
epoch 3 training_cost=1.737371, training_error=0.489724, dev_error=0.474726, test_error=0.479321, learning_rate=0.080000, execution_time(s)=78.390865
epoch 4 training_cost=1.610313, training_error=0.461962, dev_error=0.464242, test_error=0.465437, learning_rate=0.080000, execution_time(s)=78.282750
epoch 5 training_cost=1.521487, training_error=0.442533, dev_error=0.455979, test_error=0.457632, learning_rate=0.080000, execution_time(s)=77.166886
epoch 6 training_cost=1.452035, training_error=0.426761, dev_error=0.451179, test_error=0.453436, learning_rate=0.080000, execution_time(s)=77.064029
epoch 7 training_cost=1.394820, training_error=0.413627, dev_error=0.443357, test_error=0.445354, learning_rate=0.080000, execution_time(s)=78.169549
epoch 8 training_cost=1.347145, training_error=0.402646, dev_error=0.441773, test_error=0.444214, learning_rate=0.080000, execution_time(s)=77.795720
epoch 9 training_cost=1.305390, training_error=0.392546, dev_error=0.437266, test_error=0.443972, learning_rate=0.080000, execution_time(s)=77.853706
epoch 10 training_cost=1.269022, training_error=0.383710, dev_error=0.434375, test_error=0.442246, learning_rate=0.080000, execution_time(s)=78.647257
epoch 11 training_cost=1.235830, training_error=0.375467, dev_error=0.431452, test_error=0.433421, learning_rate=0.080000, execution_time(s)=77.407901
epoch 12 training_cost=1.205622, training_error=0.368032, dev_error=0.432220, test_error=0.434976, learning_rate=0.080000, execution_time(s)=78.074666
epoch 13 training_cost=1.132139, training_error=0.348587, dev_error=0.419605, test_error=0.423768, learning_rate=0.040000, execution_time(s)=77.901673
epoch 14 training_cost=1.098085, training_error=0.339950, dev_error=0.418413, test_error=0.423302, learning_rate=0.040000, execution_time(s)=78.078263
epoch 15 training_cost=1.079947, training_error=0.335365, dev_error=0.418103, test_error=0.424390, learning_rate=0.040000, execution_time(s)=77.930880
epoch 16 training_cost=1.042311, training_error=0.325323, dev_error=0.412053, test_error=0.417673, learning_rate=0.020000, execution_time(s)=78.131381
epoch 17 training_cost=1.025281, training_error=0.320353, dev_error=0.413433, test_error=0.418553, learning_rate=0.020000, execution_time(s)=78.066443
epoch 18 training_cost=1.004788, training_error=0.314823, dev_error=0.408852, test_error=0.415479, learning_rate=0.010000, execution_time(s)=79.046657
epoch 19 training_cost=0.995931, training_error=0.312059, dev_error=0.409269, test_error=0.414081, learning_rate=0.010000, execution_time(s)=77.593635
epoch 20 training_cost=0.985299, training_error=0.309571, dev_error=0.407089, test_error=0.412613, learning_rate=0.005000, execution_time(s)=78.187198
epoch 21 training_cost=0.980231, training_error=0.308210, dev_error=0.406648, test_error=0.412423, learning_rate=0.005000, execution_time(s)=78.114028
epoch 22 training_cost=0.977153, training_error=0.307378, dev_error=0.406378, test_error=0.413494, learning_rate=0.005000, execution_time(s)=78.196592
epoch 23 training_cost=0.970285, training_error=0.305204, dev_error=0.405064, test_error=0.412578, learning_rate=0.002500, execution_time(s)=80.574895
epoch 24 training_cost=0.968486, training_error=0.304418, dev_error=0.406182, test_error=0.412734, learning_rate=0.002500, execution_time(s)=78.166218

4. Kaldi Decoding.

During the last epoch, the training script creates a file pout_test.ark containing a set of likelihoods (i.e., normalized posterior probabilities) computed on the test sentences. These likelihoods can be used to feed the Kaldi decoder in this way:

cd kaldi_decoding_scripts

For mfcc features:

 ./decode_dnn_TIMIT.sh $KALDI_ROOT/egs/timit/s5/exp/tri3/graph \
 $KALDI_ROOT/egs/timit/s5/data/test/ \
 $KALDI_ROOT/egs/timit/s5/exp/dnn4_pretrain-dbn_dnn_ali \
 ../TIMIT_MLP_mfcc/decoding_test \
 "cat ../TIMIT_MLP_mfcc/pout_test.ark"

For fMLLR features

 ./decode_dnn_TIMIT.sh $KALDI_ROOT/egs/timit/s5/exp/tri3/graph \
 $KALDI_ROOT/egs/timit/s5/data/test/ \
 $KALDI_ROOT/egs/timit/s5/exp/dnn4_pretrain-dbn_dnn_ali \
 ../TIMIT_MLP_fmllr/decoding_test \
 "cat ../TIMIT_MLP_fmllr/pout_test.ark"

5. Check the results.

  • After that training and decoding phases are finished, you can go into the pytorch_MLP_for_ASR folder and run ./RESULTS to check the system performance.

If everything if fine, you should obtain Phone Error Rates (PER%) similar to the following ones:

  • mfcc features: PER=18.0%
  • fMLLR features: PER=16.8%

For reference purposes, you can take a look to our results here: TIMIT_MLP_fmllr_reference or TIMIT_MLP_mfcc_reference.

Note that, despite its simplicity, the performance obtained with this implementation is slightly better than that achieved with the kaldi baselines (even without pre-training or sMBR). For comparison purposes, see for instance the file $KALDI_ROOT/egs/timit/s5/RESULTS.

Note also that small variations of PER with respect to these reference values (e.g, +/- 0.5 %) are normal (since due to a different DNN initialization).

Reference:

Please, cite my PhD thesis if you use this code:

[1] M. Ravanelli, "Deep Learning for Distant Speech Recognition", PhD Thesis, Unitn 2017

https://arxiv.org/abs/1712.06086

About

This code implements a basic MLP for speech recognition. The MLP is trained with pytorch, while feature extraction, alignments, and decoding are performed with Kaldi. The current implementation supports dropout and batch normalization. An example for phoneme recognition using the standard TIMIT dataset is provided.

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